SynthesisBMC pregnancy and childbirth2022
Machine learning to predict pregnancy outcomes: a systematic review, synthesizing framework and future research agenda.
Synthesis in BMC pregnancy and childbirth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
54 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrating aqli (rationale) and naqli (revealed) knowledge in AI-driven clinical decision support systems to enhance healthcare delivery for guiding termination of pregnancy among Muslim patients: a systematic review (1).BMC medical informatics and decision making · 2026Pooled it
- Machine learning for risk stratification of hypertensive disorders of pregnancy: Enhancing clinical efficiency in low-resource antenatal care in Tanzania.PLOS digital health · 2026Article
- Development and Evaluation of an Adaptive Penguin-Improved LSTM Model Integrating Natural Language Processing for Early Prediction of Pregnancy Syndrome Risks.Birth defects research · 2026Observational
- High-risk pregnancy prediction using Taguchi-optimized machine learning methods and TOPSIS-based model selection.Scientific reports · 2026Article
- Risk factors for drug-related gestational diabetes mellitus: a real-world pharmacovigilance study based on the FAERS database.BMC pregnancy and childbirth · 2026Article
- AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- The use of machine learning to predict pharmacological therapy in gestational diabetes: A scoping review.Diabetic medicine : a journal of the British Diabetic Association · 2026Review
- Artificial intelligence for predicting and preventing adverse pregnancy outcomes addressing bias and clinical translation.Frontiers in digital health · 2026Review
- Factors associated with prior pregnancy loss among multigravid women: a retrospective machine learning analysis from an African population cohort.Frontiers in global women's health · 2026Article
- Artificial intelligence in primary ovarian insufficiency management: opportunities and challenges.Journal of assisted reproduction and genetics · 2025Review
- Predicting stillbirth and identifying key maternal risk factors using machine learning.BMJ paediatrics open · 2025Article
- Machine learning-augmented biomarkers in mid-pregnancy Down syndrome screening improve prediction of small-for-gestational-age infants.Orphanet journal of rare diseases · 2025Article
- Development and validation of a machine learning model for prediction of cephalic dystocia.BMC pregnancy and childbirth · 2025Article
- Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review.Journal of medical Internet research · 2025Article
- Integrating Artificial Intelligence into Perinatal Care Pathways: A Scoping Review of Reviews of Applications, Outcomes, and Equity.Nursing reports (Pavia, Italy) · 2025Review
- The role of artificial intelligence in maternal and child health: Progress, controversies, and future directions.PLOS digital health · 2025Review
- Developing and validating an artificial intelligence-based application for predicting some pregnancy outcomes: a multi-phase study protocol.Reproductive health · 2025Article
- Artificial Intelligence's Role in Improving Adverse Pregnancy Outcomes: A Scoping Review and Consideration of Ethical Issues.Journal of clinical medicine · 2025Review
- Artificial intelligence: A novel tool for diagnosing and managing kidney problems in pregnant women.International journal of reproductive biomedicine · 2025Article
- The effect of myo-inositol on assisted reproductive technology outcomes in women with polycystic ovarian syndrome: A systematic review and meta-analysis of randomized clinical trial studies.International journal of reproductive biomedicine · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine Learning (ML) has been widely used in predicting the mode of childbirth and assessing the potential maternal risks during pregnancy. The primary aim of this review study is to explore current research and development perspectives that utilizes the ML techniques to predict the optimal mode of childbirth and to detect various complications during childbirth. A total of 26 articles (published between 2000 and 2020) from an initial set of 241 articles were selected and reviewed following a Systematic Literature Review (SLR) approach. As outcomes, this review study highlighted the objectives or focuses of the recent studies conducted on pregnancy outcomes using ML; explored the adopted ML algorithms along with their performances; and provided a synthesized view of features used, types of features, data sources and its characteristics. Besides, the review investigated and depicted how the objectives of the prior studies have changed with time being; and the association among the objectives of the studies, uses of algorithms, and the features. The study also delineated future research opportunities to facilitate the existing initiatives for reducing maternal complacent and mortality rates, such as: utilizing unsupervised and deep learning algorithms for prediction, revealing the unknown reasons of maternal complications, developing usable and useful ML-based clinical decision support systems to be used by the expecting mothers and health professionals, enhancing dataset and its accessibility, and exploring the potentiality of surgical robotic tools. Finally, the findings of this review study contributed to the development of a conceptual framework for advancing the ML-based maternal healthcare system. All together, this review will provide a state-of-the-art paradigm of ML-based maternal healthcare that will aid in clinical decision-making, anticipating pregnancy problems and delivery mode, and medical diagnosis and treatment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.